Identifying value conscious users

ABSTRACT

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors to perform operations comprising: identifying a segment of users who are value conscious about a product by: evaluating a number of activities of the users indicating whether or not the users are value conscious for the product; and generating, using a conditional probability equation, a probability that a user of the users will show interest in the product; and transmitting instructions to display, for viewing by the user, a recommendation for the product, wherein the recommendation is based on the probability being above a threshold that the user will show interest in the product. Other embodiments are disclosed herein.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation application of U.S. patentapplication Ser. No. 16/260,472, filed on Jan. 29, 2019, which claimsthe benefit of U.S. Provisional Application No. 62/623,454, filed onJan. 29, 2018. U.S. Provisional Application No. 62/623,454 and U.S.patent application Ser. No. 16/260,472 are incorporated herein byreference in its entirety.

TECHNICAL FIELD

This disclosure relates generally to human preferences for value whenpurchasing a value-sensitive product through on-line websites.

BACKGROUND

Websites display content for users that is regularly changing. Manyvalue conscious users select content on websites based on pricing andvalue.

BRIEF DESCRIPTION OF THE DRAWINGS

To facilitate further description of the embodiments, the followingdrawings are provided in which:

FIG. 1 illustrates a front elevational view of a computer system that issuitable for implementing an embodiment of the system disclosed in FIG.3-6 ;

FIG. 2 illustrates a representative block diagram of an example of theelements included in the circuit boards inside a chassis of the computersystem of FIG. 1 ;

FIG. 3 illustrates a block diagram of a system that can be employed fordetermining users that are value conscious when purchasing a product;

FIG. 4 illustrates a flow chart for a method, according to anotherembodiment;

FIG. 5 illustrates a representative block diagram for determining userbrand affinities for a product or product brand, according to theembodiment of FIG. 4 ; and

FIG. 6 illustrates a flow chart for a method, according to anotherembodiment.

For simplicity and clarity of illustration, the drawing figuresillustrate the general manner of construction, and descriptions anddetails of well-known features and techniques may be omitted to avoidunnecessarily obscuring the present disclosure. Additionally, elementsin the drawing figures are not necessarily drawn to scale. For example,the dimensions of some of the elements in the figures may be exaggeratedrelative to other elements to help improve understanding of embodimentsof the present disclosure. The same reference numerals in differentfigures denote the same elements.

The terms “first,” “second,” “third,” “fourth,” and the like in thedescription and in the claims, if any, are used for distinguishingbetween similar elements and not necessarily for describing a particularsequential or chronological order. It is to be understood that the termsso used are interchangeable under appropriate circumstances such thatthe embodiments described herein are, for example, capable of operationin sequences other than those illustrated or otherwise described herein.Furthermore, the terms “include,” and “have,” and any variationsthereof, are intended to cover a non-exclusive inclusion, such that aprocess, method, system, article, device, or apparatus that comprises alist of elements is not necessarily limited to those elements, but mayinclude other elements not expressly listed or inherent to such process,method, system, article, device, or apparatus.

The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,”“under,” and the like in the description and in the claims, if any, areused for descriptive purposes and not necessarily for describingpermanent relative positions. It is to be understood that the terms soused are interchangeable under appropriate circumstances such that theembodiments of the apparatus, methods, and/or articles of manufacturedescribed herein are, for example, capable of operation in otherorientations than those illustrated or otherwise described herein.

The terms “couple,” “coupled,” “couples,” “coupling,” and the likeshould be broadly understood and refer to connecting two or moreelements mechanically and/or otherwise. Two or more electrical elementsmay be electrically coupled together, but not be mechanically orotherwise coupled together. Coupling may be for any length of time,e.g., permanent or semi-permanent or only for an instant. “Electricalcoupling” and the like should be broadly understood and includeelectrical coupling of all types. The absence of the word “removably,”“removable,” and the like near the word “coupled,” and the like does notmean that the coupling, etc. in question is or is not removable.

As defined herein, two or more elements are “integral” if they arecomprised of the same piece of material. As defined herein, two or moreelements are “non-integral” if each is comprised of a different piece ofmaterial.

As defined herein, “approximately” can, in some embodiments, mean withinplus or minus ten percent of the stated value. In other embodiments,“approximately” can mean within plus or minus five percent of the statedvalue. In further embodiments, “approximately” can mean within plus orminus three percent of the stated value. In yet other embodiments,“approximately” can mean within plus or minus one percent of the statedvalue.

IDENTIFYING VALUE CONSCIOUS USERS Description of Examples of Embodiments

Turning to the drawings, FIG. 1 illustrates an exemplary embodiment of acomputer system 100, all of which or a portion of which can be suitablefor (i) implementing part or all of one or more embodiments of thetechniques, methods, and systems and/or (ii) implementing and/oroperating part or all of one or more embodiments of the non-transitorycomputer readable media described herein and/or operating part or all ofone more embodiments of the memory storage modules described herein. Asan example, a different or separate one of a computer system 100, all ofwhich or a portion of which can be suitable for (i) implementing part orall of one or more embodiments of the techniques, methods, and systemsand/or (ii) implementing and/or operating part or all of one or moreembodiments non-transitory computer readable media described herein. Asan example, a different or separate one of computer system 100 (and itsinternal components, or one or more elements of computer system 100) canbe suitable for implementing part or all of the techniques, methods,and/or systems described herein. Furthermore, one or more elements ofcomputer system 100 (e.g., a monitor 106, a keyboard 104, and/or a mouse110, etc.) also can be appropriate for implementing part or all of oneor more embodiments of the techniques, methods, and/or systems describedherein. Computer system 100 can comprise chassis 102 containing one ormore circuit boards (not shown), a Universal Serial Bus (USB) port 112,a Compact Disc Read-Only Memory (CD-ROM) and/or Digital Video Disc (DVD)drive 116, and a hard drive 114. A representative block diagram of theelements included on the circuit boards inside chassis 102 is shown inFIG. 2 . A central processing unit (CPU) 210 in FIG. 2 is coupled to asystem bus 214 in FIG. 2 . In various embodiments, the architecture ofCPU 210 can be compliant with any of a variety of commerciallydistributed architecture families.

Continuing with FIG. 2 , system bus 214 also is coupled to a memorystorage unit 208, where memory storage unit 208 can comprise (i)non-volatile memory, such as, for example, read only memory (ROM) and/or(ii) volatile memory, such as, for example, random access memory (RAM).The non-volatile memory can be removable and/or non-removablenon-volatile memory. Meanwhile, RAM can include dynamic RAM (DRAM),static RAM (SRAM), etc. Further, ROM can include mask-programmed ROM,programmable ROM (PROM), one-time programmable ROM (OTP), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable ROM (EEPROM) (e.g., electrically alterable ROM (EAROM)and/or flash memory), etc. In these or other embodiments, memory storageunit 208 can comprise (i) non-transitory memory and/or (ii) transitorymemory.

In various examples, portions of the memory storage module(s) of thevarious embodiments disclosed herein (e.g., portions of the non-volatilememory storage module(s)) can be encoded with a boot code sequencesuitable for restoring computer system 100 (FIG. 1 ) to a functionalstate after a system reset. In addition, portions of the memory storagemodule(s) of the various embodiments disclosed herein (e.g., portions ofthe non-volatile memory storage module(s)) can comprise microcode suchas a Basic Input-Output System (BIOS) operable with computer system 100(FIG. 1 ). In the same or different examples, portions of the memorystorage module(s) of the various embodiments disclosed herein (e.g.,portions of the non-volatile memory storage module(s)) can comprise anoperating system, which can be a software program that manages thehardware and software resources of a computer and/or a computer network.The BIOS can initialize and test components of computer system 100 (FIG.1 ) and load the operating system. Meanwhile, the operating system canperform basic tasks such as, for example, controlling and allocatingmemory, prioritizing the processing of instructions, controlling inputand output devices, facilitating networking, and managing files.Exemplary operating systems can comprise one of the following: (i)Microsoft® Windows® operating system (OS) by Microsoft Corp. of Redmond,Wash., United States of America, (ii) Mac® OS X by Apple Inc. ofCupertino, Calif., United States of America, (iii) UNIX® OS, and (iv)Linux® OS. Further exemplary operating systems can comprise one of thefollowing: (i) the iOS® operating system by Apple Inc. of Cupertino,Calif., United States of America, (ii) the Blackberry® operating systemby Research In Motion (RIM) of Waterloo, Ontario, Canada, (iii) theWebOS operating system by LG Electronics of Seoul, South Korea, (iv) theAndroid™ operating system developed by Google, of Mountain View, Calif.,United States of America, (v) the Windows Mobile™ operating system byMicrosoft Corp. of Redmond, Wash., United States of America, or (vi) theSymbian™ operating system by Accenture PLC of Dublin, Ireland.

As used herein, “processor” and/or “processing module” means any type ofcomputational circuit, such as but not limited to a microprocessor, amicrocontroller, a controller, a complex instruction set computing(CISC) microprocessor, a reduced instruction set computing (RISC)microprocessor, a very long instruction word (VLIW) microprocessor, agraphics processor, a digital signal processor, or any other type ofprocessor or processing circuit capable of performing the desiredfunctions. In some examples, the one or more processing modules of thevarious embodiments disclosed herein can comprise CPU 210.

Alternatively, or in addition to, the systems and procedures describedherein can be implemented in hardware, or a combination of hardware,software, and/or firmware. For example, one or more application specificintegrated circuits (ASICs) can be programmed to carry out one or moreof the systems and procedures described herein. For example, one or moreof the programs and/or executable program components described hereincan be implemented in one or more ASICs. In many embodiments, anapplication specific integrated circuit (ASIC) can comprise one or moreprocessors or microprocessors and/or memory blocks or memory storage.

In the depicted embodiment of FIG. 2 , various I/O devices such as adisk controller 204, a graphics adapter 224, a video controller 202, akeyboard adapter 226, a mouse adapter 206, a network adapter 220, andother I/O devices 222 can be coupled to system bus 214. Keyboard adapter226 and mouse adapter 206 are coupled to keyboard 104 (FIGS. 1-2 ) andmouse 110 (FIGS. 1-2 ), respectively, of computer system 100 (FIG. 1 ).While graphics adapter 224 and video controller 202 are indicated asdistinct units in FIG. 2 , video controller 202 can be integrated intographics adapter 224, or vice versa in other embodiments. Videocontroller 202 is suitable for monitor 106 (FIGS. 1-2 ) to displayimages on a screen 108 (FIG. 1 ) of computer system 100 (FIG. 1 ). Diskcontroller 204 can control hard drive 114 (FIGS. 1-2 ), USB port 112(FIGS. 1-2 ), and CD-ROM drive 116 (FIGS. 1-2 ). In other embodiments,distinct units can be used to control each of these devices separately.

In some embodiments, network adapter 220 can be suitable to connectcomputer system 100 (FIG. 1 ) to a computer network by wiredcommunication (e.g., a wired network adapter) and/or wirelesscommunication (e.g., a wireless network adapter). In some embodiments,network adapter 220 can be plugged or coupled to an expansion port (notshown) in computer system 100 (FIG. 1 ). In other embodiments, networkadapter 220 can be built into computer system 100 (FIG. 1). For example,network adapter 220 can be built into computer system 100 (FIG. 1 ) bybeing integrated into the motherboard chipset (not shown), orimplemented via one or more dedicated communication chips (not shown),connected through a PCI (peripheral component interconnector) or a PCIexpress bus of computer system 100 (FIG. 1 ) or USB port 112 (FIG. 1 ).

Returning now to FIG. 1 , although many other components of computersystem 100 are not shown, such components and their interconnection arewell known to those of ordinary skill in the art. Accordingly, furtherdetails concerning the construction and composition of computer system100 and the circuit boards inside chassis 102 are not discussed herein.

When computer system 100 is running, program instructions (e.g.,computer instructions) stored on one or more of the memory storagemodule(s) of the various embodiments disclosed herein can be executed byCPU 210 (FIG. 2 ). At least a portion of the program instructions,stored on these devices, can be suitable for carrying out at least partof the techniques and methods described herein.

Further, although computer system 100 is illustrated as a desktopcomputer in FIG. 1 , there can be examples where computer system 100 maytake a different form factor while still having functional elementssimilar to those described for computer system 100. In some embodiments,computer system 100 may comprise a single computer, a single server, ora cluster or collection of computers or servers, or a cloud of computersor servers. Typically, a cluster or collection of servers can be usedwhen the demand on computer system 100 exceeds the reasonable capabilityof a single server or computer. In certain embodiments, computer system100 may comprise a portable computer, such as a laptop computer. Incertain other embodiments, computer system 100 may comprise a mobileelectronic device, such as a smartphone. In certain additionalembodiments, computer system 100 may comprise an embedded system.

Turning ahead in the drawings, FIG. 3 illustrates a block diagram of asystem 300 that can be employed for determining whether some users arevalue conscious users (e.g., shoppers) for a given value-sensitiveproduct or value-sensitive product category as described in greaterdetail below. System 300 is merely exemplary and embodiments of thesystem are not limited to the embodiments presented herein. System 300can be employed in many different embodiments or examples notspecifically depicted or described herein. In some embodiments, certainelements or modules of system 300 can perform various procedures,processes, and/or activities. In these or other embodiments, theprocedures, processes, and/or activities can be performed by othersuitable elements or modules of system 300.

Generally, therefore, system 300 can be implemented with hardware and/orsoftware, as described herein. In some embodiments, part or all of thehardware and/or software can be conventional, while in these or otherembodiments, part or all of the hardware and/or software can becustomized (e.g., optimized) for implementing part or all of thefunctionality of system 300 described herein.

In some embodiments, retailers can desire to use efficient and accuratemethods and systems in which to provide relevant products for users thatpurchase products on-line (e.g., browsing the webpages of the website ofa retailer) or at a physical store (e.g., brick and mortar store)location of a retailer. In many embodiments, retailers can be interestedin determining a segment and/or population of users that frequentlypurchase certain products minus a price discount, such asvalue-sensitive products, and still purchase other products independentof a price discount, and can provide at least relevant promotions,notifications, discounts, and/or other recommendations. In someembodiments, products having a price discount can be determined to bevalue-sensitive products. In many embodiments, non-brand products, thatcan include generic brand products and/or store brand products, can besold at prices below a related or a similar brand of products also canbe determined to be value-sensitive products. In several embodiments,identifying such segments of users (e.g., shoppers, buyers) can providechallenges in the identification of the segment of users as users (e.g.,shoppers, buyers) often can purchase both value-sensitive products andproducts without a price discount. Additionally, users (e.g., shoppers,buyers) often browse webpages for both value-sensitive products andproducts without a price discount during a single session during asingle session, therefore identification of such a segment of users canbe far from a straightforward approach to accumulate and/or analyzedata.

In many embodiments, a method or system can be presented based ontracking certain user features or activities associated with shoppingfor value-sensitive products that can efficiently and accuratelydetermine and/or predict probabilities that can define such a segmentand/or population of users that can be determined to be value conscioususers (e.g., shoppers) for certain value-sensitive products. In anotherembodiment, the features and/or activities of users in the aggregate canbe tracked regularly and evaluated using at least methods of conditionalprobabilities and specific algorithms corresponding to value-sensitiveproducts.

In many embodiments, identifying a segment or population of users thatcan be determined to be value conscious for certain products also can bedetermined to be value-sensitive products. The terms product and itemare exemplary and used interchangeably to refer to any product or anyitem for sale. The terms product or item, in the singular or pluraltense, are merely exemplary and embodiments for determining a product oritem can be employed in many different embodiments or by many differentexamples.

In several embodiments, by tracking user activities in the aggregate,retailers can determine a group of users that have affinities forpurchasing certain products or product categories. In variousembodiments, based on tracking user activities in the aggregate,retailers can determine a group of users that can have affinities forpurchasing certain products or product categories, such asvalue-sensitive products.

In many embodiments, various methods and systems can be used by aretailer that can determine whether a user frequently purchases, browsesa webpage, and/or prefers to purchase certain products, such asvalue-sensitive products, by tracking at least one or more useractivities, user behavior, user preferences, and user purchasesassociated with a product, an item, a product category, an itemcategory, and/or other consumer purchases.

In a number of embodiments, users (e.g., shoppers, buyers) thatfrequently purchase a value-sensitive product (including a service)on-line (e.g., while browsing a webpage or website) and/or in a physicalstore (e.g., brick and mortar store) can be determined to be valueconscious for the value-sensitive product, and more specifically, usersthat frequently purchase a value-sensitive product on-line and/or in aphysical store (e.g., brick and mortar store) exceeding a pre-determinednumber of times within a pre-determined period of time can be determinedto be value conscious for certain value-sensitive products and/or forthe product purchased. In some embodiments, users that frequentlypurchase a value-sensitive product in a value-sensitive product categoryon-line or in a physical store (e.g., brick and mortar store) exceedinga pre-determined number of times within a pre-determined period of timecan be determined to be value conscious for the value-sensitive productcategory.

In various embodiments, users that do not frequently purchase avalue-sensitive product (including a service) on-line (e.g., whilebrowsing a webpage or website) and/or in a physical store (e.g., brickand mortar store) can be determined not to be value conscious for avalue-sensitive product, and more specifically, users that purchase avalue-sensitive product on-line or in a physical store (e.g., brick andmortar store) below a pre-determined number of times within apre-determined period of time can be determined not to be valueconscious for the product purchased. In some embodiments, users thatpurchase a value-sensitive product in a value-sensitive product categoryon-line or in a physical store (e.g., brick and mortar store) below apre-determined number of times within a pre-determined period of timecan be determined to not be value conscious for the value-sensitiveproduct category.

In some embodiments, retailers can determine when a user is valueconscious for a value-sensitive product by using conditionalprobabilities to calculate the interest a user has for a certainproduct. In several embodiments, the conditional probability equationscan be based on at least one feature, or user activity, among manyfeatures and activities comprising items purchased, items searched for,items clicked, items co-bought, webpages visited, geographic location,income level, category information, product descriptions, productreviews, user profiles, and/or external websites, as discussed ingreater detail below.

In many embodiments, a value-sensitive product can be a product whereinthe price of the product can be discounted for sale. In someembodiments, a value-sensitive product can be a non-brand product,including a generic brand product and/or a store brand product, when theprice of the non-brand product can be below the price of a name brandproduct with similar product descriptions and product attributes. Inseveral embodiments, a value-sensitive non-brand product can be ageneric version of a brand product independent of any price differencebetween the generic version of the brand product and the correspondingbrand product.

In some embodiments, system 300 can include a user information system310, a web server 320, a display system 360, and/or a productinformation system 370. In several embodiments, user information system310, web server 320, display system 360, and/or product informationsystem 370 can each be a computer system, such as computer system 100(FIG. 1 ), as described above, and can each be a single computer, asingle server, or a cluster or collection of computers or servers, or acloud of computers or servers. In a number of embodiments, a singlecomputer system can host each of two or more of user information system310, web server 320, display system 360, and/or product informationsystem 370. Additional details regarding user information system 310,web server 320, display system 360, and/or product information system370 are described herein.

In many embodiments, system 300 also can comprise user computers 340,341. In certain embodiments, user computers 340-341 can be desktopcomputers, laptop computers, a mobile device, and/or other endpointdevices used by one or more users 350 and 351, respectively. A mobiledevice can refer to a portable electronic device (e.g., an electronicdevice easily conveyable by hand by a person of average size) with thecapability to present audio and/or visual data (e.g., text, images,videos, music, etc.). For example, a mobile device can include at leastone of a digital media player, a cellular telephone (e.g., asmartphone), a personal digital assistant, a handheld digital computerdevice (e.g., a tablet personal computer device), a laptop computerdevice (e.g., a notebook computer device, a netbook computer device), awearable user computer device, or another portable computer device withthe capability to present audio and/or visual data (e.g., images,videos, music, etc.). Thus, in many examples, a mobile device caninclude a volume and/or weight sufficiently small as to permit themobile device to be easily conveyable by hand. For examples, in someembodiments, a mobile device can occupy a volume of less than or equalto approximately 1790 cubic centimeters, 2434 cubic centimeters, 2876cubic centimeters, 4056 cubic centimeters, and/or 5752 cubiccentimeters. Further, in these embodiments, a mobile device can weighless than or equal to 15.6 Newtons, 17.8 Newtons, 22.3 Newtons, 31.2Newtons, and/or 44.5 Newtons.

Exemplary mobile electronic devices can comprise (i) an iPod®, iPhone®,iTouch®, iPad®, MacBook® or similar product by Apple Inc. of Cupertino,Calif., United States of America, (ii) a Blackberry® or similar productby Research in Motion (RIM) of Waterloo, Ontario, Canada, (iii) a Lumia®or similar product by the Nokia Corporation of Keilaniemi, Espoo,Finland, and/or (iv) a Galaxy™ or similar product by the Samsung Groupof Samsung Town, Seoul, South Korea. Further, in the same or differentembodiments, a mobile electronic device can comprise an electronicdevice configured to implement one or more of (i) the iPhone® operatingsystem by Apple Inc. of Cupertino, Calif., United States of America,(ii) the Blackberry® operating system by Research In Motion (RIM) ofWaterloo, Ontario, Canada, (iii) the Palm® operating system by Palm,Inc. of Sunnyvale, Calif., United States, (iv) the Android™ operatingsystem developed by the Open Handset Alliance, (v) the Windows Mobile™operating system by Microsoft Corp. of Redmond, Wash., United States ofAmerica, or (vi) the Symbian™ operating system by Nokia Corp. ofKeilaniemi, Espoo, Finland.

Further still, the term “wearable user computer device” as used hereincan refer to an electronic device with the capability to present audioand/or visual data (e.g., text, images, videos, music, etc.) that isconfigured to be worn by a user and/or mountable (e.g., fixed) on theuser of the wearable user computer device (e.g., sometimes under or overclothing; and/or sometimes integrated with and/or as clothing and/oranother accessory, such as, for example, a hat, eyeglasses, a wristwatch, shoes, etc.). In many examples, a wearable user computer devicecan comprise a mobile electronic device, and vice versa. However, awearable user computer device does not necessarily comprise a mobileelectronic device, and vice versa.

In specific examples, a wearable user computer device can comprise ahead mountable wearable user computer device (e.g., one or more headmountable displays, one or more eyeglasses, one or more contact lenses,one or more retinal displays, etc.) or a limb mountable wearable usercomputer device (e.g., a smart watch). In these examples, a headmountable wearable user computer device can be mountable in closeproximity to one or both eyes of a user of the head mountable wearableuser computer device and/or vectored in alignment with a field of viewof the user.

In more specific examples, a head mountable wearable user computerdevice can comprise (i) Google Glass™ product or a similar product byGoogle Inc. of Menlo Park, Calif., United States of America; (ii) theEye Tap™ product, the Laser Eye Tap™ product, or a similar product byePI Lab of Toronto, Ontario, Canada, and/or (iii) the Raptyr™ product,the STAR 1200™ product, the Vuzix Smart Glasses M100™ product, or asimilar product by Vuzix Corporation of Rochester, N.Y., United Statesof America. In other specific examples, a head mountable wearable usercomputer device can comprise the Virtual Retinal Display™ product, orsimilar product by the University of Washington of Seattle, Wash.,United States of America. Meanwhile, in further specific examples, alimb mountable wearable user computer device can comprise the iWatch™product, or similar product by Apple Inc. of Cupertino, Calif., UnitedStates of America, the Galaxy Gear or similar product of Samsung Groupof Samsung Town, Seoul, South Korea, the Moto 360 product or similarproduct of Motorola of Schaumburg, Ill., United States of America,and/or the Zip™ product, One™ product, Flex™ product, Charge™ product,Surge™ product, or similar product by Fitbit Inc. of San Francisco,Calif., United States of America.

In some embodiments, web server 320 can be in data communication throughInternet 330 with user computers (e.g., 340, 341). In certainembodiments, user computers 340-341 can be desktop computers, laptopcomputers, smart phones, tablet devices, and/or other endpoint devices.Web server 320 can host one or more websites. For example, web server320 can host a website that allows users to browse and/or search forproducts, to add products to an electronic shopping cart, and/or topurchase products, in addition to other suitable activities.

In many embodiments, user information system 310, web server 320,display system 360, and/or product information system 370 can eachcomprise one or more input devices (e.g., one or more keyboards, one ormore keypads, one or more pointing devices such as a computer mouse orcomputer mice, one or more touchscreen displays, a microphone, etc.),and/or can each comprise one or more display devices (e.g., one or moremonitors, one or more touch screen displays, projectors, etc.). In theseor other embodiments, one or more of the input device(s) can be similaror identical to keyboard 104 (FIG. 1 ) and/or a mouse 110 (FIG. 1 ).Further, one or more of the display device(s) can be similar oridentical to monitor 106 (FIG. 1 ) and/or screen 108 (FIG. 1 ). Theinput device(s) and the display device(s) can be coupled to theprocessing module(s) and/or the memory storage module(s) of userinformation system 310, web server 320, display system 360, and/orproduct information system 370 in a wired manner and/or a wirelessmanner, and the coupling can be direct and/or indirect, as well aslocally and/or remotely. As an example of an indirect manner (which mayor may not also be a remote manner), a keyboard-video-mouse (KVM) switchcan be used to couple the input device(s) and the display device(s) tothe processing module(s) and/or the memory storage module(s). In someembodiments, the KVM switch also can be part of user information system310, web server 320, display system 360, and/or product informationsystem 370. In a similar manner, the processing module(s) and the memorystorage module(s) can be local and/or remote to each other.

In many embodiments, user information system 310, web server 320,display system 360, and/or product information system 370 can beconfigured to communicate with one or more user computers 340 and 341.In some embodiments, user computers 340 and 341 also can be referred toas user computers. In some embodiments, user information system 310, webserver 320, display system 360, and/or product information system 370can communicate or interface (e.g., interact) with one or more usercomputers (such as user computers 340 and 341) through a network orinternet 330. Internet 330 can be an intranet that is not open to thepublic. Accordingly, in many embodiments, user information system 310,web server 320, display system 360, and/or product information system370 (and/or the software used by such systems) can refer to a back endof system 300 operated by an operator and/or administrator of system300, and user computers 340 and 341 (and/or the software used by suchsystems) can refer to a front end of system 300 used by one or moreusers 350 and 351, respectively. In some embodiments, users 350 and 351also can be referred to as users, in which case, user computers 340 and341 can be referred to as user computers. In these or other embodiments,the operator and/or administrator of system 300 can manage system 300,the processing module(s) of system 300, and/or the memory storagemodule(s) of system 300 using the input device(s) and/or displaydevice(s) of system 300.

Meanwhile, in many embodiments, user information system 310, web server320, display system 360, and/or product information system 370 also canbe configured to communicate with one or more databases. The one or moredatabases can comprise a product database that contains informationabout products, items, or SKUs (stock keeping units) sold by a retailer.The one or more databases can be stored on one or more memory storagemodules (e.g., non-transitory memory storage module(s)), which can besimilar or identical to the one or more memory storage module(s) (e.g.,non-transitory memory storage module(s)) described above with respect tocomputer system 100 (FIG. 1 ). Also, in some embodiments, for anyparticular database of the one or more databases, that particulardatabase can be stored on a single memory storage module of the memorystorage module(s), and/or the non-transitory memory storage module(s)storing the one or more databases or the contents of that particulardatabase can be spread across multiple ones of the memory storagemodule(s) and/or non-transitory memory storage module(s) storing the oneor more databases, depending on the size of the particular databaseand/or the storage capacity of the memory storage module(s) and/ornon-transitory memory storage module(s).

The one or more databases can each comprise a structured (e.g., indexed)collection of data and can be managed by any suitable databasemanagement systems configured to define, create, query, organize,update, and manage database(s). Exemplary database management systemscan include MySQL (Structured Query Language) Database, PostgreSQLDatabase, Microsoft SQL Server Database, Oracle Database, SAP (Systems,Applications, & Products) Database, and IBM DB2 Database.

Meanwhile, communication between user information system 310, web server320, display system 360, product information system 370 and/or the oneor more databases can be implemented using any suitable manner of wiredand/or wireless communication. Accordingly, system 300 can comprise anysoftware and/or hardware components configured to implement the wiredand/or wireless communication. Further, the wired and/or wirelesscommunication can be implemented using any one or any combination ofwired and/or wireless communication network topologies (e.g., ring,line, tree, bus, mesh, star, daisy chain, hybrid, etc.) and/or protocols(e.g., personal area network (PAN) protocol(s), local area network (LAN)protocol(s), wide area network (WAN) protocol(s), cellular networkprotocol(s), powerline network protocol(s), etc.). Exemplary PANprotocol(s) can comprise Bluetooth, Zigbee, Wireless Universal SerialBus (USB), Z-Wave, etc.; exemplary LAN and/or WAN protocol(s) cancomprise Institute of Electrical and Electronic Engineers (IEEE) 802.3(also known as Ethernet), IEEE 802.11 (also known as WiFi), etc.; andexemplary wireless cellular network protocol(s) can comprise GlobalSystem for Mobile Communications (GSM), General Packet Radio Service(GPRS), Code Division Multiple Access (CDMA), Evolution-Data Optimized(EV-DO), Enhanced Data Rates for GSM Evolution (EDGE), Universal MobileTelecommunications System (UMTS), Digital Enhanced CordlessTelecommunications (DECT), Digital AMPS (IS-136/Time Division MultipleAccess (TDMA)), Integrated Digital Enhanced Network (iDEN), EvolvedHigh-Speed Packet Access (HSPA+), Long-Term Evolution (LTE), WiMAX, etc.The specific communication software and/or hardware implemented candepend on the network topologies and/or protocols implemented, and viceversa. In many embodiments, exemplary communication hardware cancomprise wired communication hardware including, for example, one ormore data buses, such as, for example, universal serial bus(es), one ormore networking cables, such as, for example, coaxial cable(s), opticalfiber cable(s), and/or twisted pair cable(s), any other suitable datacable, etc. Further exemplary communication hardware can comprisewireless communication hardware including, for example, one or moreradio transceivers, one or more infrared transceivers, etc. Additionalexemplary communication hardware can comprise one or more networkingcomponents (e.g., modulator-demodulator components, gateway components,etc.).

In many embodiments, an internal network that is not open to the publiccan be used for communications between user information system 310, webserver 320, display system 360, product information system 370. In someembodiments, the same or another internal network can be used forcommunications between user information system 310, web server 320,display system 360, product information system 370. Accordingly, in someembodiments, user information system 310, web server 320, display system360, product information system 370 (and/or the software used by suchsystems) can refer to a back end of system 300 operated by an operatorand/or administrator of system 300, and web server 320 (and/or thesoftware used by such systems) can refer to a front end of system 300,as is can be accessed and/or used by one or more users, such as users350-351, using user computers 340-341, respectively. In these or otherembodiments, the operator and/or administrator of system 300 can managesystem 300, the processor(s) of system 300, and/or the memory storageunit(s) of system 300 using the input device(s) and/or display device(s)of system 300.

Turning ahead in the drawings, FIG. 4 illustrates a flow chart for amethod 400, according to an embodiment. Method 400 is merely exemplaryand is not limited to the embodiments presented herein. Method 400 canbe employed in many different embodiments or examples not specificallydepicted or described herein. In some embodiments, the activities ofmethod 400 can be performed in the order presented. In otherembodiments, the activities of method 400 can be performed in anysuitable order. In still other embodiments, one or more of theactivities of method 400 can be combined or skipped. In manyembodiments, system 300 (FIG. 3 ) can be suitable to perform method 400and/or one or more of the activities of method 400. In these or otherembodiments, one or more of the activities of method 400 can beimplemented as one or more computer instructions configured to run atone or more processing modules and configured to be stored at one ormore non-transitory memory storage modules 506 (FIG. 5 ). Suchnon-transitory memory storage modules can be part of a computer systemsuch as user information system 310 (FIGS. 3 & 5 ) and/or display system360 (FIGS. 3 & 5 ). The processing module(s) can be similar or identicalto the processing module(s) described above with respect to computersystem 100 (FIG. 1 ) and computer system 200 (FIG. 2 ).

In some embodiments, method 400 and other blocks in method 400 caninclude using a distributed network including distributed memoryarchitecture to perform the associated activity. This distributedarchitecture can reduce the impact on the network and system resourcesto reduce congestion in bottlenecks while still allowing data to beaccessible from a central location.

Referring to FIG. 4 , in many embodiments, method 400 can include block405 for determining first users who can be determined to be valueconscious about a first value-sensitive product. In a number ofembodiments, websites and retailers can attract different types ofusers. In several embodiments, some of the users can be those who can beinterested in paying a lower price for a particular product minus aprice discount (i.e., value conscious). In some embodiments, websitescan be able to tailor marketing and/or recommendations that can helpsome of these users save money and can lead to a better quality of life.In a number of embodiments, identifying some of these segments of userscan be less than straightforward. In some embodiments, an algorithm thatcan utilize personal information can help identify these users. In manyembodiments, some users who tend to purchase frequently value-sensitiveproducts can be value conscious users. Examples of value-sensitiveproducts are provided below. In several embodiments, users can be valueconscious on some value-sensitive products at certain times, while atother times, they are not price sensitive to the same and/or differentprices or costs for the same and/or different products independent of aprice discount.

In some embodiments, block 405 can include a user (e.g., shopper, buyer)who can be interested in purchasing a certain computer with certainproduct attributes, brands, and/or product descriptions. In variousembodiments, a user can spend money on high-end hardware, and at thesame time, the same user is not interested in purchasing high-endcookware, but can be interested in purchasing cookware from inexpensivebrands. In several embodiments, various methods and systems canincorporate value consciousness for some users for a given and/or aparticular item using a conditional probability that a user can be avalue conscious user. In many embodiments, some methods and systems candetermine that a user can be a value conscious user by using aconditional probability equation that can be formally calculatedexpressed as p(vc|c, i) where p is probability, vc is valueconsciousness, c is a particular user of interest and i is an item ofinterest. Some embodiments use this information to aggregate thisinformation and can derive value consciousness of a given user. Inseveral embodiments, a sequence of equations can be expressed below,wherein each sequential equation builds from the previous equationculminating to the end equation (1) expressed as:

p(vc|c)≈Σ_(i∈Items) p(vc|c,i)·p(i|c)  (1)

where each sequential equation listed below is not based on theactivities and/or behavior of c, a particular user of interest.Additionally, Items refers to a set or sets of items that can be sold ata retail store, or a set or sets of items that the retailer can beinterested in promoting, wherein a variable on the upper limit isimplied. The sequential expanding equations for p(vc|c) as expressed:

$\begin{matrix}{{p\left( {vc} \middle| c \right)} = \frac{\sum_{i \in {Items}}{{p\left( {\left. {vc} \middle| c \right.,i} \right)} \cdot {p\left( {c,i} \right)}}}{p(c)}} \\{= \frac{\sum_{i \in {Items}}{{p\left( {\left. {vc} \middle| c \right.,i} \right)} \cdot {p\left( c \middle| i \right)} \cdot {p(i)}}}{p(c)}} \\{= \frac{\sum_{i \in {Items}}{{p\left( {\left. {vc} \middle| c \right.,i} \right)} \cdot {p\left( c \middle| i \right)} \cdot \frac{p(c)}{p(i)}}}{p(c)}}\end{matrix}$

In this expanding equation sequence, p(i) and p(c) are constant, wherep(i) is probability of the item of interest and p(c) is the probabilityof the particular user of interest.

In some embodiments, block 405 can be based on some of these intuitionswhere the conditional probability that a particular user c will likelyshow interest in the item i based on particular feature from acombination of features wherein the resulting derivation of p(vc|c, i)can be calculated. Some embodiments can be defined as p(vc|c, i) using afeature from a list of features f in particular, wherein a feature oractivity of a particular user of interest refers to the intention,interest, or preference of the particular user for an item of interest,as can be expressed in the equation below:

$\begin{matrix}{{{p\left( {\left. {vc} \middle| c \right.,i} \right)} = {\sum_{f}{{w_{f} \cdot p}\left( {\left. {vc} \middle| c \right.,f} \right)}}},,} & (2)\end{matrix}$ ${\sum\limits_{f}w_{f}} = 1$

where w_(f) denotes a weight for a feature f, and p (vc|c, f) denotesthe value consciousness of the particular user for an item derived inthe equation using a single feature f of a list of features listed belowwherein the list of features are exemplary features.

In many embodiments using the above conditional probability equation,where w_(f) denotes a weight for a feature f, the equation can set w_(f)based on a hill climbing algorithm by optimizing towards revenue. Inseveral embodiments, the hill climbing algorithm can be adjusted usingdata gathered from a click through rate, and/or an engagement rate,depending on requirements of the retailer. In some embodiments, for somefeatures f that can be less than dependent on users can be expressed asa conditional probability in this equation: p(vc|c, f)=p(vc|f). Somefeatures that are utilized in this conditional probability are based onsome of the following activities of the user expressing intention,interest, or preference for an item, wherein this list of features ismerely exemplary in nature and can include: items bought, items searchedfor, items clicked, items co-bought, pages visited, geographicallocation, income level, category information, product descriptions,product reviews, user profiles, and referred external websites.

In a number of embodiments, method 400 can include a block 410 fordetermining second users who can be determined not to be value consciousabout the first product. In some embodiments, users who do not tend tofrequently purchase value-sensitive products can be users who can bedetermined not to be value conscious. In many embodiments, some userscan be value conscious on some products, while at other times they canbe sensitive not to the difference in price or costs between someproducts.

In some embodiments, method 400 can include a block 415 analyzingon-line shopping histories of the first users and the second users. Insome embodiments, the shopping histories can include on-line shoppinghistories and/or physical store (e.g., brick and mortar store) shoppinghistories. In many embodiments, websites and retailers can registerand/or store various user or user profiles based on the purchasingbehaviors and/or purchasing histories of users or users associated withone or more retailers. In some embodiments, user profiles can includeadditional user information, such as, whether the user has children, thegender or age of the children, the age and/or gender of the user,hobbies, pets, and/or many other ways that can describe users. Inseveral embodiments, user profiles can have different measures of valueconsciousness. In a number of embodiments, users can act differentlydepending on a persona a user can take on. As an example, some users canpurchase expensive products when buying pet related products, butpurchase cheaper products when they take on a different home persona.

In some embodiments, method 400 can include block 415 for a given searchquery, q, the user can have ultimately clicked on an item i. This can begiven as p(i|q). In some embodiments using the results from the previousfeatures section, p(vc|q) can be derived as:

p(vc|q)=Σ_(i∈Items) p(vc|i)·(i|q)  (3)

wherein, p is for probability and vc is for value consciousness.

In some embodiments, block 415, can be associated with queries q′ϵQ forsearch query q. The method can be generic enough that such approachescan be used for these purposes.

In a number of embodiments, method 400 can include a block 420 ofanalyzing on-line shopping patterns of the first users and the secondusers. In some embodiments, the shopping patterns can include on-lineshopping patterns, on-line browsing activity, and/or physical store(e.g., brick and mortar store) shopping patterns. In some embodiments,some webpages of websites of one or more retailers can be moreindicative of value consciousness than other webpages where a webpagecomprising value-sensitive items can be a webpage browsed by usersdetermined to be value conscious. In several embodiments, when a webpageleads to a value-sensitive product, this webpage can be considered as avalue conscious webpage. In some embodiments, differences with productpurchases can be on one webpage that can lead into another webpageresulting in webpage trails, so these webpage trails and/or modelingtrails comprising user activity can be taken into account to determinewhether a user can be value conscious for a value-sensitive product.

Modeling trails ultimately can be defined as the following:

P(e|g ₁)=π_(i) p(g _(i|gi−1))·p(e|g _(n))  (4)

where e can be the end state and/or a final page visited in a given webbrowsing session, g_(i) can be the ith page the user can be visiting ina given web browsing session, g_(n) is the nth page in a session, g₁ isthe first page visited in a given web browsing session, and p is forprobability. In some cases e can be whether a user has ended the sessionin a value-sensitive product or not. In some embodiments, ultimately, wecan have p(vc|g₁)=p(i|g₁)·p(vc|i). In some embodiments, a given page cantake a features page type (item page, category page, search page,homepage), and can include latent and/or hidden labels of the page(e.g., webpage).

Some embodiments, block 420 can utilize sequential labeling techniques,wherein a label can comprise a page (e.g., webpage) a user is visiting,to derive P(e|g₁). There are various methods to learn sequential labels,for instance, Hidden Markov Models (HMM), Conditional Random Fields(CRF), or Long Short Term Memory (LSTM) are some examples of these.

In some embodiments, method 400 can include a block 425 of retrievingproduct information from a website database to identify the firstproduct with a value price tag. In several embodiments, some websitesand retailers can have various tags assigned to products. In variousembodiments, some value price tags can include rollback tags, clearancetags, special buy tags, and/or other such value price tags. In a numberof embodiments, value price tags can be indicative of lower prices. Inmany embodiments, some websites and retailers can indicate products withthese types of price tags to be value-sensitive products. In someembodiments, as noted above, some users who tend to purchase frequentlyfrom value-sensitive products can be value conscious users, and someusers can be value conscious on some products, while at other times theycan be less than sensitive to different costs or differences in pricesfor a product.

In some embodiments where method 400 includes blocks 415 and 420, itemsco-bought and/or co-clicked can be further expanded by the term p (vc|i,c) by the products that can be co-bought and/or co-clicked, wherein p isfor probability, i is for item, and c is a particular user. In severalembodiments, a commonly used method can be used that can captureco-bought and/or co-clicked item that can be a matrix factorization. Inmany embodiments, an algorithm can leverage low-rank matrices that caninfer latent features for some given users and items. In variousembodiments, some latent features can be used to calculate the behaviorof some users. In many embodiments, some behaviors can be a rating ofsome products from the perspective of some users, and/or it can be aninteraction between some products and some users. Some embodiments canextend this framework, and can instead infer the value-product-nessand/or value-sensitivity of some given item. Some embodiments can recallthat p(vc|i)=Σ_(t)p(vc|t)·p(t|i), wherein, p is for probability, vc isfor value consciousness, i is for item, and t is for tag. If someembodiments can infer the latent p(t|i) then some embodiments cancalculate its value-product-ness of some given item. Some embodimentscan formulate the following equation, as expressed:

min_(U,I,T) ∥V−U·I∥+∥W−I·T∥+∥U∥+∥I∥+∥T∥  (5)

where U, I, and T can be U for a user, I for an item, and T for a tagmatrices, respectively. V is for product purchases and/or clicks whilebrowsing a website and/or webpages and W is for the probability ofp(t|i) where V and W can be pre-populated with a purchase history of auser and tags for an item. After the computation, some embodiments canbe find tag probability W.

In several embodiments, method 400 can include blocks 415 and 420 ofanalyzing shopping histories and shopping patterns of a user based on afeature f, wherein p (vc|c,f), refers to value-sensitive items purchasedand/or clicked on when browsing a website or webpage of a retailer,wherein p is for probability, vc is for value consciousness, c is aparticular user, and t is for tag. In many embodiments, an item can havevalue-sensitive tags wherein the determination of when an item can be avalue-sensitive item comprises the addition of one or more tagsassociated with the item. In some embodiments, the tags can compriseclearances, special buys, rollbacks, and other price discount tags. Thetags can be assigned the probability p(vc|t) where t is the tag ofinterest, p is for probability, and vc is for value consciousness. Inseveral embodiments, using the probability p(vc|t), a probabilityequation can be generated to determine whether this product isvalue-sensitive or not. In many embodiments, a product that isvalue-sensitive can be flagged as value-sensitive where the conditionalprobability can be derived as p(vc|i) as further discussed below,wherein p is for probability, vc is for value consciousness, and i isfor item.

In some embodiments, method 400 also can include a block 425 ofretrieving product information from a website database to identify theproduct with a value price tag. In several embodiments, avalue-sensitive item can have value conscious tags (clearance, specialbuy, rollback) associated with it. In some embodiments, some tags can beassigned p(vc|t) where t can be the tag of interest, p is forprobability, and vc is for value consciousness. In several embodiments,using tags can be used to generate a flag regarding whether this productcan be value-sensitive or not. Ultimately some embodiments can derivep(vc|i), wherein p is for probability, vc is for value consciousness,and i is for item. Some embodiments can already have p(t|i) and in usingit can have:

p(vc|i)=Σ_(t) p(vc|t)·p(t|i)  (6)

wherein, p is for probability, vc is for value consciousness, i is foritem, and t is for tag.

In some embodiments, block 425 can be associated with some productdescriptions and attributes that also can be used to detect valueconsciousness. In many embodiments, product descriptions and attributesthat also can be used to detect value consciousness can be particularlyuseful for products that can be infrequently purchased and/or when awebsite and/or retailer can expand to a wider array of products. In anumber of embodiments that already have p(vc|i), product descriptionsand attributes that also can be used to detect value consciousness canbe valid for items that have high purchase counts. In many embodiments,on the other hand, product descriptions, attributes and reviews can tendto talk about similar attribute-value pairs. For example, in someembodiments a user can say a product is “good for the price,” but thisparticular comment can be used on products across many differentcategories. Similarly, some attributes can be more effective for somegiven demographics over the others. In some embodiments, by learningcommon descriptions and attributes for value conscious users websitesand retailers can be able to leverage these features instead when notenough users had purchased the product. In various embodiments, websitesand retailers can extend the feature set from tag-specific tonon-tag-specific products.

For some products that sold at least a minimum number exceeding apre-determined threshold with a pre-determined period of time, websitesand retailers get p(vc|i), expressed as:

p(vc|a)=Σ_(i∈Items) p(vc|i)·p(i|a)  (7)

where a can be the attribute, p is for probability, vc is for valueconsciousness, and i is for item.

In some embodiments, using this framework, websites and retailers canderive attributes corresponding to some product descriptions and someproduct reviews. In several embodiments, word embedding can allow one tofind latent features for a given word. In some embodiments, someexamples of these include Word2Vec, PCA, and LDA, to name a few. Inseveral embodiments, the word embedding can output distribution ofprobabilities over latent features. In many embodiments, the latent orhidden features for a product can be denoted as p(a|i), which can bethen derived p(i|a) using Bayesian probabilities wherein p isprobability, a is for an attribute of interest, and i is for an item ofinterest.

In a number of embodiments, method 400 can include a block 430 ofdetermining whether to display one or more first recommendations andpromotions for the first product to the first user. In severalembodiments, a determining can be based on the user profile of the firstuser. In some embodiments, block 430 can include determining whether tonot display the one or more first recommendations and promotions for thefirst product to the second user. As an example, the firstrecommendations and promotions can include a list of value-sensitiveproducts, additional price discounts for the value-sensitive products,early notifications of sales, early notifications when a value tag isadded to an item, reminders, coupon codes, and/or other price discounts.

In some embodiments, method 400 can include a block 440 of determiningwhether to display one or more second recommendations and promotions forthe first product to the second user. In many embodiments, a determiningcan be based on the user profile of the second user. In someembodiments, block 440 can include determining whether to not displayone or more second recommendations and promotions for the first productto the first user. As examples, the second recommendations andpromotions can be similar to the first recommendations and promotionsand the first recommendations and promotions can be for larger discountsand savings than for the second recommendations and promotions.

In many embodiments, some websites and retailers can provide varioususer profiles based on purchasing behavior of users. In a number ofembodiments, user profiles can cover (e.g., include) whether the userhas children, the gender or age of the children, the age or gender ofusers, hobbies, pets, and/or the many other ways to describe users. Insome embodiments, user profiles have different measures of valueconsciousness. In several embodiments, users (e.g., shoppers, buyers)can act differently depending on the persona that they can take. As anexample, some users can purchase expensive products when buying petrelated products, but purchase cheaper products when they take a newhome persona. The terms user and user are exemplary and usedinterchangeably to refer to any user or user of the system that conductsactivities such as purchasing items, browsing for items, searching foritems, requesting queries for items and/or other engagement activityrelated to shopping in general. The terms user or user item, in thesingular or plural, are merely exemplary and can include products and/orservices.

In some embodiments, to realize this goal, some websites and retailerscan calculate value consciousness for a given profile and users. Inparticular, as expressed:

$\begin{matrix}{{p\left( {\left. {vc} \middle| c \right.,r} \right)} = \frac{\left( {{p(c)} \cdot {p\left( {vc} \middle| c \right)}} \right) \cdot \left( {{p(r)} \cdot {p\left( {vc} \middle| r \right)}} \right)}{{p\left( r \middle| c \right)} \cdot {p(c)}}} & (8)\end{matrix}$

where r can be a given user profile or user profile, p is forprobability, vc is for value consciousness and c is a particular user.In some embodiments, user profiles can have assigned scores which can berepresented in p(r|c). We can infer p(vc|r) by aggregating profiles ofusers who can belong in this category. In particular, as expressed:

$\begin{matrix}{{p\left( {vc} \middle| r \right)} = \frac{\sum_{c \in R}{{p\left( {vc} \middle| c \right)} \cdot {p(c)}}}{p(r)}} & (9)\end{matrix}$

where p is for probability, vc is for value consciousness, c is for aparticular user, and R is for the set of users at the websites andretailers.

In some embodiments, an equation can give (e.g., express) the likelihoodthat a particular user profile can belong to a value conscious category.In some embodiments, user profile features also can be expanded tocategory affinities that can show how likely a user can be to view,click or purchase from a given category. In some embodiments, similarprocesses can be run as can be given in user profile features to derivecategory features.

In a number of embodiments, method 400 can include a block 445 ofpreparing the first recommendations and/or promotions for a first user.In some embodiments, block 445 can occur before blocks 430 and/or 440.In the same or different embodiments, block 445 and/or a different block(not shown) in method 400 can include preparing the secondrecommendations and/or promotions for the second user.

In some embodiments, method 400 can include a block 450 of transmittingmachine readable instructions to display the one or more firstrecommendations and/or promotions for the first product for the firstuser. In some embodiments, block 450 can include not transmittingmachine readable instructions to display the one or more firstrecommendations and promotions for the first product for the seconduser.

In a number of embodiments, method 400 can include a block 455 oftransmitting machine readable instructions to display the one or moresecond recommendations and promotions for the first product for thesecond user. In some embodiments, block 455 can include not transmittingmachine readable instructions to display the one or more secondrecommendations and promotions for the first product for the first user.

Turning to the drawings, FIG. 5 illustrates a block diagram of a portionof system 300 comprising user information system 310, web server 320,display system 360, and product information system 370, according to theembodiment shown in FIG. 3 . Each of user information system 310, webserver 320, display system 360, and/or product information system 370 ismerely exemplary and not limited to the embodiments presented herein.Each of user information system 310, web server 320, display system 360,and/or product information system 370 can be employed in many differentembodiments or examples not specifically depicted or described herein.In some embodiments, certain elements or modules of user informationsystem 310, web server 320, display system 360, and/or productinformation system 370 can perform various procedures, processes, and/oracts. In other embodiments, the procedures, processes, and/or acts canbe performed by other suitable elements or modules. In many embodiments,the systems of user information system 310, web server 320, displaysystem 360, and/or product information system 370 can be modules ofcomputing instructions (e.g., software modules) stored at non-transitorycomputer readable media. In other embodiments, the systems of userinformation system 310, web server 320, display system 360, and/orproduct information system 370 can be implemented in hardware.

In many embodiments, modules within user information system 310, webserver 320, display system 360, and/or product information system 370can store computing instructions configured to run on one or moreprocessing modules and perform one or more acts of methods 400 (FIG. 4 )(e.g., transaction system 512 for analyzing physical store (e.g., brickand mortar store) shopping histories of first users and second users inblock 415 (FIG. 4 ); website system 514 for analyzing website shoppinghistories of the first users and the second users in block 415 (FIG. 4); display processor system 562 for transmitting machine readableinstructions to display the first recommendations and promotions for thefirst product for viewing by the first user in block 450 (FIG. 4 ), andfor transmitting readable instructions to display the secondrecommendations and promotions for the first product for viewing by thesecond user in block 455 (FIG. 4 ); inventory system 572 for retrievingproduct information from a website database to identify the firstproduct with a value tag in block 425 (FIG. 4 ); and productrecommendation system 574 for determining whether to display firstrecommendations to the first user in block 430 (FIG. 4 ) and whether todisplay second recommendations to the second user in block 440 (FIG. 4), and for preparing the first and second recommendations and promotionsto the first user and the second user in block 445 (FIG. 4 ).)

In a number of embodiments, user information system 310 can include atransaction system 512 and a website system 514. In certain embodiments,transaction system 512 and website system 514 can perform block 405(FIG. 4 ) of determining first users who are value conscious about afirst product. In certain embodiments, transaction system 512 andwebsite system 514 also can perform block 410 (FIG. 4 ) of determiningsecond users who are not value conscious about the first product. Incertain embodiments, transaction system 512 and website system 514 canperform block 415 (FIG. 4 ) of analyzing shopping histories of the firstusers and the second users. In certain embodiments, transaction system512 and website system 514 can perform block 420 (FIG. 4 ) of analyzingshopping patterns of the first users and the second users.

In some embodiments, product information system 370 can includeinventory system 572. In certain embodiments, inventory system 572 canperform block 425 (FIG. 4 ) of retrieving product information from awebsite database to identify the first product with a value price tag.

In a number of embodiments, product information system 370 also caninclude product recommendation system 574. In certain embodiments,product recommendation system 574 can perform block 430 (FIG. 4 ) ofdetermining whether to display first recommendations and promotions forthe first product to the first user. In some embodiments, productrecommendation system 574 also can perform block 440 (FIG. 4 )determining whether to display second recommendations and promotions forthe first product based on the user profile of the second user. In someembodiments, product recommendation system 574 can also perform block445 (FIG. 4 ) preparing the first and second recommendations andpromotions for the first user and the second user, respectively.

In a number of embodiments, display system 360 can include displayprocessor system 562. In some embodiments, display processor system 562can perform block 430 (FIG. 4 ) of determining whether to display firstrecommendations and promotions for the first product to the first user.In some embodiments, display processor system 562 also can perform block440 (FIG. 4 ) determining whether to display second recommendations andpromotions for the first product based on the user profile of the seconduser. In some embodiments, display processor system 562 can performblock 450 (FIG. 4 ) transmitting machine readable instructions todisplay the first recommendations and promotions for the first productfor viewing by the first user. In some embodiments, display processorsystem 562 also can perform block 455 (FIG. 4 ) transmitting machinereadable instructions to display the second recommendations andpromotions for the first product for viewing by the second user.

Turning ahead to the drawing, FIG. 6 , illustrates a flowchart for oneor more conditional probabilities using various features andcombinations of features in the equations set forth above where it canbe determined based on one or more features or activities of a user 601and whether a user 601 can be value conscious 650 for a value-sensitiveitem. (See at least equation (1)). In some embodiments, block 600 can bea non-limiting and non-exhaustive embodiment depicting the method and/orsystem whereupon any one or more activities can be tracked and analyzedto determine whether or not some users 601 can be categorized as valueconscious users (e.g., shoppers) 650, 660, corresponding to certainvalue-sensitive products based on the above one or more conditionalprobability equations.

For example, the block 600 depicts a user 601 engaging in variousfeatures or activities for a given value-sensitive product. Block 600 ismerely exemplary and is not limited to the embodiments presented herein.Block 600 can be employed in many different embodiments or examples notspecifically depicted or described herein. In some embodiments theprocedures, the processes, and/or the activities of block 600 can beperformed in any suitable order. In still other embodiments, one or moreof the procedures, the processes, and/or the activities of block 600 canbe combined or skipped.

In many embodiments, when a user 601 engages in features or activities anumber of times exceeding a pre-determined threshold during apre-determined period of time, it can be determined that the user can bevalue conscious 650 for a given value-sensitive product wherein aretailer can recommend more value and/or value-sensitive products 660 tothe user 601. In several embodiments, when a user 601 engages infeatures or activities a number of times below a pre-determinedthreshold during a pre-determined period of time, it can be determinedthat the user can be not value conscious 650, 670, for a given valueand/or value-sensitive product wherein a retailer can recommend productsbased on the user or user profile stored with the retailer for the user601.

In another embodiment of FIG. 6 , user 601 purchases value and/orvalue-sensitive products 610. (See at least equations (1), (3), (4),(5), and (6).) Block 600 can extract data comprising value-sensitiveattributes 611 and value-sensitive products 615 from purchases and/orthe purchase history of the user (e.g., shopper, buyer). Further block611, can compare the extracted value-sensitive product attributesassociated with the value-sensitive item purchased by the user or user601 with a database of the retailer that stores aggregated data of otherusers that previously purchased the value-sensitive item with theattribute 612, the value-sensitive item co-purchased with anotherrelated item in the on-line session represented by block 613, and thevalue-sensitive item co-clicked on a website or webpage with other itemsduring an on-line session represented by block 614.

Block 612 can synchronize value-sensitive products with block 613regarding one or more co-purchased products and block 614 regardingco-clicked products when the value-sensitive items with certainattributes shown in block 612 was co-purchased with other items in asession represented by block 613 and/or co-clicked with other items in asession represented by block 614. Blocks 610, 611, 615 can utilize atleast equation (1) to perform a conditional probability calculation todetermine whether the user can be value conscious for a value-sensitiveproduct or value-sensitive products 650. When the level of probabilityexceeds a pre-determined threshold for a pre-determined period of time,it can be determined that the user can be likely value conscious forcertain value-sensitive products analyzed using the blocks of 611, 612,613, 614, 615, wherein block 660 can recommend at least additionalvalue-sensitive products, other related value-sensitive products, earlynotifications of price discounts, reminders, and/or additional discountsfor the value-sensitive product. In many embodiments, when the level ofprobability is below a pre-determined threshold for a pre-determinedperiod of time, it can be determined that the user is likely not valueconscious for certain value-sensitive products analyzed using the blocks611, 612, 613, 614, 615, wherein block 670 can recommend other itemsand/or price discounts for the other items that correspond to theprofile data in the user profile stored in a user database of theretailer.

In another embodiment of FIG. 6 , user 601 views a website or webpagefor value-sensitive products 630, 635 during an on-line session. (See atleast equations (1) (3), (4), (5), (6), and (7)). Block 600 can extractdata comprising value-sensitive attributes 611 and value-sensitiveproducts 615 from purchases and/or the purchase history of the user oruser. Further block 611, can compare the extracted value-sensitiveproduct attributes associated with the value-sensitive item purchased bythe user or user 601 with a database of the retailer that storesaggregated data of other users or users that previously purchased thevalue-sensitive item with the attribute(s), as shown in block 612, thevalue-sensitive item co-purchased with another related item in theon-line session represented by block 613, and the value-sensitive itemco-clicked on a website or webpage with other items during an on-linesession represented by block 614.

Block 612 can synchronize value-sensitive products with block 613regarding one or more co-purchased products and block 614 regarding oneor more co-clicked products when the value-sensitive items with certainattributes, as shown in block 612, was co-purchased with other itemsduring a session represented by block 613 and/or co-clicked with otheritems in a session represented by block 614. In several embodiments,blocks of 610, 611, 615, 635 utilize at least equations (1), (5), and(7) to perform a conditional probability calculation to determinewhether the user can be value conscious for a value-sensitive product orvalue-sensitive products 650. In some embodiments, when the level ofprobability exceeds a pre-determined threshold for a pre-determinedperiod of time, it can be determined that the user can likely be valueconscious for certain value-sensitive products analyzed using the blocksof 611, 612, 613, 614, 615, wherein block 660 can recommend at leastadditional value-sensitive products, other related value-sensitiveproducts, early notifications of price discounts, reminders, and/oradditional discounts for the value-sensitive product. In manyembodiments, when the level of probability is below a pre-determinedthreshold for a pre-determined period of time, it can be determined thatthe user is likely not value conscious for certain value-sensitiveproducts analyzed using the blocks of 611, 612, 613, 614, 615, whereinblock 670 can recommend other items or price discounts for the otheritems that correspond to the profile data in the user profile stored ina user database of the retailer.

Turning to another embodiment of FIG. 6 , user 601 performs one or moresearches using queries searching or requesting various products on awebsite and webpages of a retailer of block 620 during an on-linesession. (See at least equations (1) and (3).) In some embodiments, awebsite can have a value conscious webpage when it either leads tovalue-sensitive products and/or comprises value-sensitive products, asshown in blocks 620, 625. (See at least equations (1) and (4).) In manyembodiments, block 625 can extract and analyze search queries searchingand/or requesting product information for value-sensitive productsduring a session. In various embodiments, when the level of probabilityexceeds a pre-determined threshold for a pre-determined period of time,it can be determined that the user can likely be value conscious forcertain value-sensitive products analyzed using blocks 620, 625, whereinblock 660 can recommend at least additional value-sensitive products,other related value-sensitive products, early notifications of pricediscounts, reminders, and/or additional discounts for thevalue-sensitive product. In a number of embodiments, when the level ofprobability is below a pre-determined threshold for a pre-determinedperiod of time, it can be determined that the user can be not likely tobe value conscious for certain value-sensitive products analyzed usingblocks 620, 625, wherein block 670 can recommend other items and/orprice discounts for the other items that can correspond to the profiledata in the user profile stored in a user database of the retailer.

Referring to an embodiment of FIG. 6 , user 601 can engage in browsing awebsite and clicking on various webpages of a retailer of blocks 630,635. (See at least equations (1), (4), and (5).) In many embodiments,block 635 can extract and/or analyze the value conscious webpages forvalue-sensitive products. In several embodiments, when the level ofprobability exceeds a pre-determined threshold for a pre-determinedperiod of time, it can be determined that the user is likely valueconscious for certain value-sensitive products analyzed using blocks 630and/or 635, wherein block 660 recommends at least additionalvalue-sensitive products, other related value-sensitive products, earlynotifications of price discounts, reminders, and/or additional discountsfor the value-sensitive product. In a number of embodiments, when thelevel of probability is below a pre-determined threshold for apre-determined period of time, it can be determined that the user islikely not value conscious for certain value-sensitive products analyzedusing the blocks of 630 and/or 635, wherein block 670 can recommendother items or price discounts for the other items that correspond tothe profile data in the user profile stored in a user database of theretailer.

Referring to an embodiment of FIG. 6 , user 601 provides the retailerwith a user profile based on either registering with a retailer,purchasing items on the website of the retailer and/or a physical store(e.g., brick and mortar store) location of the retailer or throughbrowsing, clicking, and searching the website and/or webpages of theretailer 640, 645. (See at least equations (1), (8), and (9).) Inseveral embodiments, a user profile comprises user information regardingmarital status, children, gender, age, hobbies, pets, geographiclocations, income levels, purchase history, browsing, clicking, andsearching history, and other descriptions of a user stored in a databaseof the retailer. In many embodiments, when a user does not possessinformation or enough information in for blocks 610, 620, and/or 630, towarrant relevant information to be added to the variables in at leastequation (1), the method or system defaults to the information stored ina user profile to determine whether a user or user is value consciousfor a value-sensitive product. In some embodiments, when the level ofprobability exceeds a pre-determined threshold for a pre-determinedperiod of time, it can be determined that the user can likely be valueconscious for certain value-sensitive products analyzed using block 645,wherein block 660 can recommend at least additional value-sensitiveproducts, other related value-sensitive products, early notifications ofprice discounts, reminders, and/or additional discounts for thevalue-sensitive product. In several embodiments, when the level ofprobability is below a pre-determined threshold for a pre-determinedperiod of time, it can be determined that the user can not likely bevalue conscious for certain value-sensitive products analyzed usingblock 645, wherein block 670 can recommend other items or pricediscounts for the other items that correspond to the profile data in theuser profile stored in a user database of the retailer.

In several embodiments, the principles described herein can be rooted incomputer technologies that can overcome existing problems in knowndatabase systems, specifically problems dealing with increasingavailable bandwidth, reducing network traffic and efficiently managingdatabases. In many embodiments, some known database systems cannothandle massive amounts of network traffic or database requests, whilekeeping latency to an acceptable level and/or avoiding server crashes.In some embodiments, the principles described in this disclosure canprovide a technical solution (e.g., one that utilizes databases in novelways) for overcoming such problems. In a number of embodiments, thistechnology-based solution can mark an improvement over existingcomputing capabilities and functionalities related to database systemsby improving bandwidth, reducing network traffic and permitting greaterdatabase efficiency (e.g., by processing combined read/delete requests).In several embodiments, some novel systems can be designed to improvethe way databases store, retrieve, delete and transmit data.

In many embodiments, the techniques described herein can provide severaltechnological improvements. Specifically, the techniques describedherein can provide for reducing network load by enabling users to findrelevant information faster. In some embodiments, by reducing thenetwork load, the system and method described herein can help to improveCPU, memory and cache performance for underlying recommendation systems.In several embodiments, this improvement can directly reduce the numberof service calls per second and can translate into better usage ofvarious system components like CPU, memory, hard disk, etc. In manyembodiments, as noted above, some methods and systems can be capable ofprocessing huge store purchase data efficiently and can allowrecommendation systems to isolate or filter users (e.g., shopper, buyer)by determining which users can be value conscious for specificvalue-sensitive products based on these models using smaller memoryfootprint.

One advantage of the techniques and/or approaches described above isthat once value conscious users can be identified, users can bepresented primarily with relevant products that can have greatvalue-sensitivity as opposed to a general pool of items, hence reducingthe number of pages (e.g., webpages) users would need to browse in orderto reach the items of specific interest (e.g., purchasing) to thosespecific users. In many embodiments, this approach can be different fromprevious approaches, which applied subjective human manualdeterminations, and/or did not involve manually annotating users who canbe value-sensitive. In several embodiments, identifying value conscioususers does not exist in other conventional approaches. In someembodiments, because this method covers the identification of usersitself, any approach of identifying item recommendations can be used.This level of personalization for transmitting recommendations andpromotions does not exist in conventional approaches to targeted e-mailsto particular groups. Moreover, because this method covers theidentification of users itself, any approach of identifying itemrecommendations can be used. Moreover, this level of personalization inthe timing of when the user can be sent the e-mail does not exist inconventional approaches, which typically transmit recommendations andpromotions to each user at a present time after a certain action, or toall of a group of users at the same time after a certain event (e.g.,after a price discount).

Additionally, the techniques described herein can run continuously basedon new information and data continually being received from actions ofusers (e.g., 350-351 (FIG. 3 )) on the website hosted by web server 320(FIG. 3 ) and the responses to the users (e.g., 350-351 (FIG. 3 )) tothe e-mails that continue to get sent out. In many embodiments, runningthese techniques continually (e.g., hourly, daily, etc.) can providereal-time determinations of which e-mail and at what time to send thee-mail to a particular user, based on the current activity (e.g., withinthe last hour, day, etc.) of the user on the website and based on theactivity of other users on the website and responses of other users tothe e-mails within the last hour, day, etc.

In many embodiments, the techniques described herein can be usedregularly (e.g., hourly, daily, etc.) at a scale that cannot be handledusing manual techniques. For example, the number of monthly visits tothe website can exceed approximately one hundred million, and the numberof registered users to the website can exceed approximately ten million.

The techniques described herein solve a technical problem that cannot besolved using more traditional forms of advertising such as direct mailvia the United States Postal Service. In fact, the techniques describedherein cannot be applied to such traditional forms of advertisingbecause any of the learning models, including logistic regression,cannot be trained in view of a lack of data, as described in greaterdetail above. For example, it would not be possible to know whether arecipient of the direct mail reviewed the direct mail and, in responseto the reviewing the direct mail, typed the web address on the directmail into the recipient's web browser to view a web page, or whether therecipient happened to view that web page due to another referral source.

In many embodiments, the techniques described herein can provide severaltechnological improvements. Specifically, the techniques describedherein can provide for improved techniques that can better provideproducts that can be of interest to users (e.g., shoppers, buyers). Insome embodiments, typical users can be interested in differentvalue-sensitive products with varying intensity. In several embodiments,users can be interested in value-sensitive products that can be sold by,or manufactured by, any given brand. This technique can improve uponconventional (e.g., existing techniques) product recommender systems. Insome embodiments, by identifying value-sensitive products of interestfor users, these systems can provide recommendations using heavierweights on certain value-sensitive products of interest of the userthereby, improving certain marketing automation techniques. In a numberof embodiments, by identifying value-sensitive products of interest tothese users, some marketers can send product advertisements and/ormessages that can best capture the varying (e.g., evolving) interests ofusers to enhance the quality of recommendation content to the specificuser. In some embodiments, by maintaining a distribution pertaining to ahigh confidence of accuracy using the scores obtained for users with anaffinity for value-sensitive products, the method and/or systems canleverage the information to transmit communications for value-sensitiveproducts using maximal precision that can reduce wasting computerresources.

In many embodiments, some conventional approaches that can be used byother recommender systems and/or other networks do not take into accountusers who can be value conscious with affinities for value-sensitiveproducts which can lead to additional inefficiencies in using computerresources when deriving relevant product recommendations displayedand/or transmitted to users, as described in greater detail above. Inseveral embodiments, given a pool of products that can be of interest tovalue conscious users, by sending the recommendations to the subset ofcustomers who can be interested in the set of particular value-sensitiveproducts can reduce the amount of computer resources often used tocalculate the same calculation for those users who are not interested inthe recommendations. In some embodiments, by reducing the computerresources used for calculations based on all categories (e.g., classes)of users regardless of an affinity value-sensitive products, the methodsand/or systems can improve upon conventional approaches by reducingoverall network usage. As an example, suppose 10% of the users can beidentified as value conscious users, wherein computing resources to runrecommendation calculations for the remaining 90% of users would nolonger be necessary for those value-sensitive products in which the 90%of users have shown no affinity towards those value-sensitive products,thereby reducing the computing requirements significantly.

In various embodiments, as previously described above, conventionalapproaches used by webserver systems and/or networks do not take intoaccount value conscious users that can lead to the inefficient use ofcomputer resources for storing relevant raw data. In some embodiments,specifically using a conventional recommender system, huge amounts ofraw data can be used to predict products that can be of interest tousers without identifying certain value-sensitive products in the data.In several embodiments, by identifying value conscious users, the hugeamount of raw data can be consolidated when used in any of the learningmodels and also can lead to using less hard disk space to store thesmaller (e.g., lesser) amounts of user data, as discussed in greaterdetail above.

A number of embodiments can include a system for identifying valueconscious users including one or more processors and one or morenon-transitory computer-readable media storing computing instructionsconfigured to run on the one or more processors and perform certainacts. The acts can include retrieving product information from a websitedatabase to identify a first product as a value-sensitive productidentified with at least a value price tag. The acts also can includedetermining first users who are value conscious about the first product.The acts can include determining second users who are not valueconscious about the first product. The acts additionally can includeanalyzing shopping histories of the first users and the second users.The acts can include analyzing shopping patterns of the first users andthe second users. The acts can also include preparing first and secondrecommendations and promotions for the first product, wherein the firstrecommendation comprises one or more value-sensitive products. The actscan include determining whether to display the first recommendations andpromotions for the first product to the first users. The acts can alsoinclude determining whether to display the second recommendations andpromotions for the first product to the second users based on userprofiles of the second users. The acts additionally can includetransmitting machine readable instructions to display the firstrecommendations and promotions for the first product for viewing by thefirst user. The acts also can include transmitting machine readableinstructions to display the second recommendations and promotions forthe first product for viewing by the second user.

Various embodiments can include a method being implemented via executionof computing instructions configured to run at one or more processorsand stored at one or more non-transitory computer-readable media. Themethod can include retrieving product information from a websitedatabase to identify a first product as a value-sensitive productidentified with at least a value price tag. The method also can includedetermining first users who are value conscious about the first product.The method can include determining second users who are not valueconscious about the first product. The method additionally can includeanalyzing shopping histories of the first users and the second users.The method can include analyzing shopping patterns of the first usersand the second users. The method can also include preparing first andsecond recommendations and promotions for the first product, wherein thefirst recommendation comprises one or more value-sensitive products. Themethod can include determining whether to display the firstrecommendations and promotions for the first product to the first users.The method can also include determining whether to display the secondrecommendations and promotions for the first product to the second usersbased on user profiles of the second users. The method additionally caninclude transmitting machine readable instructions to display the firstrecommendations and promotions for the first product for viewing by thefirst user. The method also can include transmitting machine readableinstructions to display the second recommendations and promotions forthe first product for viewing by the second user.

Several embodiments can include a system including one or moreprocessors and one or more non-transitory computer-readable mediastoring computing instructions, that when executed on the one or moreprocessors, cause the one or more processors to perform certain acts.The acts can include identifying a segment of users who are valueconscious about a product. Identifying the segment of users can includeevaluating a number of activities of the users indicating whether or notthe users are value conscious for the product. Identifying the segmentof users also can include generating, using a conditional probabilityequation, a probability that a user of the users will show interest inthe product. The conditional probability equation can be based on asequence of equations. Each sequential equation of the sequence ofequations can build upon an immediately previous equation of thesequence of equations by using data from the immediately previousequation. The acts also can include transmitting instructions todisplay, for viewing by the user, a recommendation for the product. Therecommendation can be based on the probability being above a thresholdthat the user will show interest in the product.

Many embodiments can include a method being implemented via execution ofcomputing instructions configured to run on one or more processors andstored at one or more non-transitory media. The method can includeidentifying a segment of users who are value conscious about a product.Identifying the segment of users can include evaluating a number ofactivities of the users indicating whether or not the users are valueconscious for the product. Identifying the segment of users also caninclude generating, using a conditional probability equation, aprobability that a user of the users will show interest in the product.The conditional probability equation can be based on a sequence ofequations. Each sequential equation of the sequence of equations canbuild upon an immediately previous equation of the sequence of equationsby using data from the immediately previous equation. The method alsocan include transmitting instructions to display, for viewing by theuser, a recommendation for the product. The recommendation can be basedon the probability being above a threshold that the user will showinterest in the product.

Although systems and methods for identifying value conscious users havebeen described with reference to specific embodiments, it will beunderstood by those skilled in the art that various changes may be madewithout departing from the spirit or scope of the disclosure.Accordingly, the disclosure of embodiments is intended to beillustrative of the scope of the disclosure and is not intended to belimiting. It is intended that the scope of the disclosure shall belimited only to the extent required by the appended claims. For example,to one of ordinary skill in the art, it will be readily apparent thatany element of FIGS. 1-6 may be modified, and that the foregoingdiscussion of certain of these embodiments does not necessarilyrepresent a complete description of all possible embodiments. Forexample, one or more of the procedures, processes, or activities ofFIGS. 3, 4, and 5 may include different procedures, processes, and/oractivities and be performed by many different modules, in many differentorders.

All elements claimed in any particular claim are essential to theembodiment claimed in that particular claim. Consequently, replacementof one or more claimed elements constitutes reconstruction and notrepair. Additionally, benefits, other advantages, and solutions toproblems have been described with regard to specific embodiments. Thebenefits, advantages, solutions to problems, and any element or elementsthat may cause any benefit, advantage, or solution to occur or becomemore pronounced, however, are not to be construed as critical, required,or essential features or elements of any or all of the claims, unlesssuch benefits, advantages, solutions, or elements are stated in suchclaim.

Moreover, embodiments and limitations disclosed herein are not dedicatedto the public under the doctrine of dedication if the embodiments and/orlimitations: (1) are not expressly claimed in the claims; and (2) are orare potentially equivalents of express elements and/or limitations inthe claims under the doctrine of equivalents.

What is claimed is:
 1. A system comprising: one or more processors; andone or more non-transitory computer-readable media storing computinginstructions, that when executed on the one or more processors, causethe one or more processors to perform operations comprising: identifyinga segment of users who are value conscious about a product by:evaluating a number of activities of the users indicating whether or notthe users are value conscious for the product; and generating, using aconditional probability equation, a probability that a user of the userswill show interest in the product, wherein the conditional probabilityequation is based on a sequence of equations, wherein each sequentialequation of the sequence of equations builds upon an immediatelyprevious equation of the sequence of equations by using data from theimmediately previous equation; and transmitting instructions to display,for viewing by the user, a recommendation for the product, wherein therecommendation is based on the probability being above a threshold thatthe user will show interest in the product.
 2. The system of claim 1,wherein the computing instructions, when executed on the one or moreprocessors, further cause the one or more processors to performoperations comprising: retrieving product information from a websitedatabase, wherein the product information comprises one or morevalue-sensitive product attributes derived from product reviews;identifying the product as a value-sensitive product by at least a valueprice tag for the product; detecting, using the product reviews, valueconsciousness for products that are infrequently purchased; deriving,using word embedding, latent features of each respective word of one ormore of the product reviews associated with the product and adistribution of probabilities over the latent features; and determining,using historical value-sensitive descriptions, whether each respectiveword of the product reviews associated with the product is associatedwith a value-sensitive description.
 3. The system of claim 1, whereingenerating the probability further comprises using a hill climbingalgorithm.
 4. The system of claim 1, wherein identifying the segment ofthe users further comprises identifying first users and second users,wherein the second users are not value conscious about the product. 5.The system of claim 4, wherein the computing instructions, when executedon the one or more processors, further cause the one or more processorsto perform operations comprising: analyzing shopping histories of thefirst users and the second users; and analyzing shopping patterns of thefirst users and the second users.
 6. The system of claim 5, whereinanalyzing the shopping patterns for the first users and the second usersfurther comprises: analyzing on-line shopping patterns for the firstusers and the second users, wherein the on-line shopping patternsfurther comprise at least one of: a user purchase history; a userbrowser activity; a user value conscious webpage visit; or a userprofile.
 7. The system of claim 6, wherein the computing instructions,when executed on the one or more processors, further cause the one ormore processors to perform operations comprising: leveraging low-rankmatrices to infer additional latent features for the first users, thesecond users, and one or more products to leverage low-rank matrices;and calculating, using matrix factorization, a behavior of the firstusers and the second users associated with products that are co-bought.8. The system of claim 1, wherein transmitting the instructions todisplay the recommendation for the product further comprisestransmitting instructions to update a respective webpage of a websitedisplaying a value-sensitive product.
 9. The system of claim 1, wherein:the computing instructions, when executing on the one or moreprocessors, further cause the one or more processors to performoperations comprising: preparing a first recommendation and a firstpromotion for the product; and preparing a second recommendation and asecond promotion for the product;  preparing the first recommendationand the first promotion comprises preparing larger discounts and savingsfor the first recommendation and the first promotion than for the secondrecommendation and the second promotion; and  preparing the secondrecommendation and the second promotion comprises preparing discountsand savings for the second recommendation and the second promotion basedon descriptions in user profiles of second users comprising at least oneof age, family size, pets, hobbies, geographic location, or gender. 10.The system of claim 9, wherein the computing instructions, whenexecuting on the one or more processors, further cause the one or moreprocessors to perform operations comprising: determining whether todisplay the first recommendation and the first promotion for the productto first users; and determining whether to display the secondrecommendation and the second promotion for the product to the secondusers based on user profiles of the second users.
 11. A method beingimplemented via execution of computing instructions configured to run onone or more processors and stored at one or more non-transitory media,the method comprising: identifying a segment of users who are valueconscious about a product by: evaluating a number of activities of theusers indicating whether or not the users are value conscious for theproduct; and generating, using a conditional probability equation, aprobability that a user of the users will show interest in the product,wherein the conditional probability equation is based on a sequence ofequations, wherein each sequential equation of the sequence of equationsbuilds upon an immediately previous equation of the sequence ofequations by using data from the immediately previous equation; andtransmitting instructions to display, for viewing by the user, arecommendation for the product, wherein the recommendation is based onthe probability being above a threshold that the user will show interestin the product.
 12. The method of claim 11 further comprising:retrieving product information from a website database, wherein theproduct information comprises one or more value-sensitive productattributes derived from product reviews; identifying the product as avalue-sensitive product by at least a value price tag for the product;detecting, using the product reviews, value consciousness for productsthat are infrequently purchased; deriving, using word embedding, latentfeatures of each respective word of one or more of the product reviewsassociated with the product and a distribution of probabilities over thelatent features; and determining, using historical value-sensitivedescriptions, whether each respective word of the product reviewsassociated with the product is associated with a value-sensitivedescription.
 13. The method of claim 11, wherein generating theprobability further comprises using a hill climbing algorithm.
 14. Themethod of claim 11, wherein identifying the segment of the users furthercomprises identifying first users and second users, wherein the secondusers are not value conscious about the product.
 15. The method of claim14 further comprising: analyzing shopping histories of the first usersand the second users; and analyzing shopping patterns of the first usersand the second users.
 16. The method of claim 15, wherein analyzing theshopping patterns for the first users and the second users furthercomprises: analyzing on-line shopping patterns for the first users andthe second users, wherein the on-line shopping patterns further compriseat least one of: a user purchase history; a user browser activity; auser value conscious webpage visit; or a user profile.
 17. The method ofclaim 16 further comprising: leveraging low-rank matrices to inferadditional latent features for the first users, the second users, andone or more products to leverage low-rank matrices; and calculating,using matrix factorization, a behavior of the first users and the secondusers associated with products that are co-bought.
 18. The method ofclaim 11, wherein transmitting the instructions to display therecommendation for the product further comprises transmittinginstructions to update a respective webpage of a website displaying avalue-sensitive product.
 19. The method of claim 11 further comprising:preparing a first recommendation and a first promotion for the product;and preparing a second recommendation and a second promotion for theproduct; preparing the first recommendation and the first promotioncomprises preparing larger discounts and savings for the firstrecommendation and the first promotion than for the secondrecommendation and the second promotion; and preparing the secondrecommendation and the second promotion comprises preparing discountsand savings for the second recommendation and the second promotion basedon descriptions in user profiles of second users comprising at least oneof age, family size, pets, hobbies, geographic location, or gender. 20.The method of claim 19 further comprising: determining whether todisplay the first recommendation and the first promotion for the productto first users; and determining whether to display the secondrecommendation and the second promotion for the product to the secondusers based on user profiles of the second users.